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Article

Interplay Between Online and Offline Realms: Examining Influencers’ Impact and Ripple Effects on Beauty Product Sales

by
Hee-Jin Sim
1,
Ahyun Kim
2 and
Sang-Soo Kim
1,*
1
Department of Business Administration, Yong In University, Yongin-si 17092, Republic of Korea
2
Department of Business Administration, Dongyang Mirae University, Seoul 08221, Republic of Korea
*
Author to whom correspondence should be addressed.
J. Theor. Appl. Electron. Commer. Res. 2024, 19(4), 3197-3213; https://doi.org/10.3390/jtaer19040155
Submission received: 14 September 2024 / Revised: 8 November 2024 / Accepted: 14 November 2024 / Published: 18 November 2024
(This article belongs to the Section Digital Marketing and the Connected Consumer)
Browse Figures
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<p>Results of structural model test.</p> ">
Versions Notes

Abstract

:
This study focuses on the quality of beauty product reviews as online information, aiming to determine their impact on encouraging continued product purchases by users and to explore the simultaneous ripple effects. This study derived a total of 9 hypotheses, examining the relationship between online review information quality, product purchase, and influencers who create reviews, using the expectation confirmation model and the quality–value–satisfaction–loyalty mechanism. Simultaneously, this study sought to examine the process by which the formation of trust in influencers impacts the intention to purchase other products they recommend. The research results indicate that online review quality positively influences perceived value, satisfaction, and repurchase intention when the expectations for a product match reality. Furthermore, both the confirmation of online reviews and satisfaction with the product facilitate the transition from online to offline and back, which in turn generates ripple effects. The results of this study not only add clarity to the interrelationship between online and offline contexts but also provide implications for businesses in formulating strategies to leverage influencers for product sales.

1. Introduction

Most of the information currently acquired by customers is initiated by external information generators rather than companies. This shift has occurred due to structural changes in user experiences [1]. In traditional environments, customers obtained information primarily from companies (websites, etc.). This involved one-way information production with a designated entity, much like TV, radio, and advertising. In the digital environment, customers more commonly access information generated outside the company (on social platforms). Online interactions are much more frequent and intense because anyone can generate information. This strengthens the network effect, with users at the center of that network gaining the most influence [2]. In this study, we refer to such users as influencers.
The change in information generation signifies the need for strategies to manage this shift, even though the fundamental principles or processes of product purchase remain largely unchanged. It is crucial to convey the information that companies want to deliver to customers in a friendly and easily understandable manner online. Therefore, creating high-quality content is of the utmost importance. Online information spreads much faster and more extensively than offline information, making it necessary for the content’s elements, structure, and presentation to be harmonious in order to capture users’ attention.
Traditionally, when examining a customer’s buying process, they become aware of a product or service, feel interested, develop a desire to purchase, remember it, and then take the buying action [3]. In contrast, in the era of information technology, the consumer behavior process involves becoming aware of a product or service, feeling interested, confirming information through online searches, and sharing this information with others after making a purchase. Searching and sharing represent acts of maximizing the availability of information online that can substitute for direct offline experiences. In both of these actions, information plays a pivotal role, highlighting the significance of those who create it.
The finding that the network or experience of online reviewers positively influences customers’ interest in products [4] suggests the need to examine how reviewers are perceived by customers. Previous studies have utilized the concept of reviews written by ordinary individuals rather than influencers to elaborate on individuals’ attitudes toward products [4,5], which presents a limitation, as it fails to adequately consider the characteristics of the reviewers. Given the evolving landscape, it is essential to closely investigate the concept of influencers as comprising a key online network that affects product purchases and to continuously explore the importance of influencer marketing [6].
Companies, in addressing their roles, often utilize a new online role known as influencers to construct online information. As the role of influencers with extensive and potent online networks becomes increasingly significant, prior research [7,8] has empirically shown that this role can be conceptualized in various ways and positively impact a company’s performance. However, the majority of previous studies have focused more on the influencer’s image than on interest in the information [9,10].
In this study, we aim to first define the concept of online reviews generated by influencers. While much research has explored the quantity, quality, and direction of reviews, there has been a shortage of studies addressing the overall aspects of reviews from the perspective of consumers who encounter them, specifically pertaining to the inherent attributes of the information that reviews provide. Therefore, additional research on the quality of information conveyed by reviews is warranted.
In the same context, there is a need to revisit the mechanism of review quality in the context of online information. Gallarza et al. [11] have shown that quality follows a mechanism that links to value, satisfaction, and loyalty. In this mechanism, quality is generally centered around measurements related to products or services, and there is relatively little research examining quality from an information perspective. Given the shift in the quality perspective toward information, there is a need to introduce concepts beyond directly verifying the product in the existing mechanism. This is because the nature of online platforms, where it is often not possible to directly assess the quality of a product, may necessitate a different role for this concept.
Particularly, the beauty industry places a greater emphasis on the online environment, including information, compared to other industries. This is because beauty products come into direct contact with the skin, making information even more influential. Even on comprehensive platforms that simply sell beauty products with similar functionalities and prices, a mere 0.1-point difference in review scores is significantly meaningful. However, not all beauty products are available on these comprehensive platforms, and even if they are, they might go unnoticed. Therefore, to address this issue, online information becomes a crucial purchasing factor for buyers.
This study focuses on the quality of beauty product reviews as online information, aiming to determine their impact on encouraging continued product purchases by users and to explore the simultaneous ripple effects. Accordingly, two research questions were established as follows:
Research Question 1: How does online review quality contribute to inducing repurchases of products?
Research Question 2: How does the transition from online to offline and back occur, and what are its ripple effects?
In the past, when the profession of influencers was uncommon, distinguishing influencers by the size of their follower base was important. However, with the presence of numerous influencers across various fields today, individuals within cohort groups—also described as specific clusters—follow different influencers. From this perspective, it is more appropriate to generalize and interpret an individual’s attitude toward the influencer rather than focusing on whether the influencer operates at a macro or micro level [12]. Therefore, in this study, we aimed to address the research questions without analyzing the scale of the influencer’s network effect.

2. Theoretical Background

2.1. Social Influence and Beauty Product Review Quality

Social influence explains how the behavior, thoughts, and emotions of other individuals affect an individual’s own behavior, thoughts, and emotions [13]. Contrary to traditional settings, where social influence is amplified through both verbal and non-verbal behaviors, in the online environment, users tend to trust information generated by experts or trusted sources, motivated by a desire to mitigate risks associated with uncertain information [14]. Such trusted sources can be referred to as ‘influencers’, defined as individuals with the most significant influence in a community [15]. When users check an influencer’s profile or content and feel a sense of affinity for the aspects similar to themselves, they become influenced by social factors. Hughes et al. [16] describe the informational type of social influence as the impact of information obtained from others, which is accepted as evidence for one’s reality. From this perspective, users value information because they want to minimize risks. Individuals are influenced by the information input from influential figures in online communities, leading them to engage in various actions related to both the influencer and the product.
It can be argued that the particularly high dependence on online reviews for beauty products, compared to general products, accentuates this phenomenon. Therefore, in the case of beauty products, since they are used directly on the skin, consumers are more sensitive to the quality of the product, so text, photos, videos, etc., are appropriately used in the information provided online to ensure that both the company’s purpose and the buyer’s needs are met. Wang and Strong’s [17] concept of information quality, which consists of four parts, can explain this. Intrinsic quality is a concept that considers the inherent characteristics of information necessary to judge information quality, including the accuracy, objectivity, reliability, and reputation of the content itself [17]. Contextual quality contains product functions, characteristics, and keywords that the company emphasizes in product reviews, because contextual information through specific searches plays an important role in effective information retrieval [18]. Representational quality is a concept that includes consistency of expression, concise expression, interpretability, and ease of understanding, and explains whether the contents required for the content are well organized in the review. It is considered that high-quality information is presented to users only when it can be expressed and interpreted in accordance with the purpose of the content [17]. Accessibility quality is the concept that information can be easily accessed by a variety of users [17]. This concept is constructed as a concept that constitutes beauty product review quality and is conceptualized at a level that is easily understandable and includes various information about the evaluation of online products.
Prior research has primarily conducted empirical studies that focus on individual behavior influenced by social influence, such as purchasing, using social influence [19]. However, there is a lack of exploration into how specific individual behaviors manifest within the informational category. In this study, we aim to apply this concept by extending the mechanism based on the expectation confirmation model and the quality–value–satisfaction–loyalty framework to investigate how individuals influenced by the community engage in purchasing activities, with a particular focus on the role of influencers as sources of information within the community.

2.2. Expectation Confirmation Model

Oliver [20] introduced the expectation confirmation model, which states that customer satisfaction or dissatisfaction is determined by how well their pre-purchase expectations align with their actual post-purchase experiences regarding products or services. This model enables the assessment of consumer satisfaction with various needs. Bhattacherjee [21], building upon Oliver’s [20] theory, extended this mechanism to information systems, explaining the information system usage model. In the proposed research model, perceived usefulness refers to the user’s perception of the system’s usefulness in a product sales context, describing how effectively the system can handle tasks [22]. In this study, it aims to elucidate the user’s perceived value of the product from the perspective of the system, specifically the platform.
The concept of perceived value is defined as the overall evaluation of a product, considering factors such as the quality received, the price, the associated costs and efforts, and the amount paid [22,23,24,25,26]. In particular, Zeithaml [25] primarily emphasized the economic aspect when measuring perceived value as a single dimension. However, measuring value in a single dimension is efficient but has limitations in reflecting the diverse characteristics that consumers possess [26]. Researchers have constructed the concept of perceived value in various ways. Sweeney and Soutar [27] divided it into functional value, emotional value, economical value, and social value, while Wang et al. [28] added the concept of sacrifices to the framework proposed by Sweeney and Soutar [27]. Sánchez-Fernández and Iniesta-Bonillo [29] suggested that the composition of the concept could vary depending on the perspective from which it is interpreted. They mentioned contexts such as ‘Behavioral conception’, the ‘Specific direction on how to improve value’, and the ‘Observation of value through its components’, and emphasized the need for a precise definition of value to structure the concept.
In this study, we aimed to construct a concept that can explain the value of beauty products. The value associated with beauty products is generally linked to the purpose of using the product. Many individuals use beauty products to obtain something better than what they currently have. This is generally related to the human desire to look beautiful and stylish. For example, even when using hand cream, a user would consider factors such as whether the product has a pleasant fragrance, provides sufficient moisture, and is reasonably priced for continuous use. Moreover, since they often come into contact with parts of the body and have physical or chemical effects, the quality and efficacy of the products are particularly sensitive and critical issues in consumer choices. While it is possible to include the societal aspect, where everyone is using the product [27], the current online beauty market is extensive. Consumers tend to seek personalized or customized products. Therefore, users are excluded from this perspective since they desire something unique to themselves. As a result, the concept explaining the perceived value through online product purchases is composed of three elements: functional, emotional, and economic factors. Firstly, functional value refers to the perceived utility of the product, reflecting the value placed on the expected quality before purchasing the product. Secondly, emotional value encompasses the emotions or states that consumers experience after acquiring the product. Thirdly, economic value relates to the perceived value associated with the price. The value of beauty products, as perceived by users, is defined as the combination of the product’s functionality and the feelings it evokes when used, considering whether it is reasonably priced for purchase. In this study, we intend to use perceived value as a secondary variable consisting of the three sub-variables.

2.3. Quality–Value–Satisfaction–Loyalty Framework

The quality–value–satisfaction–loyalty (Q-V-S-L) framework has been widely used in various studies [11,30]. It suggests that perceiving certain quality aspects as valuable can lead to satisfaction, ultimately positively impacting ongoing behaviors. Oliver [31,32] demonstrated the relationship between value and satisfaction, and subsequent research by Gallarza et al. [11], Granados et al. [30], and others extended the model to include Q-V-S-L relationships.
Most prior research has subdivided the concept of quality into various dimensions like service quality [33] and system quality [34]. However, this creates a perspective difference compared to research focused solely on information quality, which is the dimension explored in this study. From the perspective of selling products, service quality or system quality is typically used to explain the information provided by the company in which customers retrieve information [35]. In contrast, beauty product review quality, as studied in this research, is a concept explained in the context of externally generated information. Therefore, it needs to be reinterpreted from an information systems perspective.
Furthermore, the concept of quality in this research is distinct from the traditional notions. Quality is usually employed from the perspective of consuming products or services [36]. In this study, quality is interpreted from the perspective of individuals in the context of online information. Prior research often focused on information quality from the researcher’s experiential standpoint, leading to a shift towards user experience-centered research. Therefore, to address the mechanism of perceived information quality online, it is essential to incorporate the concept of product purchase and post-purchase experiences.
Existing research has subdivided information system quality and empirically verified its relationship with expectation confirmation [37]. Confirmation is a concept that discusses the difference between pre-purchase expectations and post-purchase outcomes [21]. Thus, the model in this study includes confirmation, extending the relationship between quality and value to encompass the actual purchase behavior.

3. Hypotheses

3.1. Online Review

We intended to begin by examining the relationship between products and influencers concerning review quality. Review quality in this context encompasses not only quantitative or qualitative aspects when understanding information in reviews, but also includes multiple dimensions of information, including the intent when selling the product. As a result, it contains more content than the concepts typically addressed in previous research on reviews [38]. Users seek to acquire as much information as possible in unpredictable situations to mitigate risks.
We can examine the relationship between beauty product review quality and confirmation. Influencers have expertise in their areas of interest, regularly sharing and expressing themselves with others [39,40]. Their influence significantly impacts consumers’ purchasing behavior. When the level of information is high, one can predict that the level of products under consideration will also be similarly high. Users strive to acquire information that encompasses various aspects (intrinsic, contextual, representation, accessibility) to ultimately minimize the difference between their expectations before seeing the product directly and the actual outcomes, as they seek to minimize risks online. Therefore, we hypothesize the following:
H1: 
The quality of a beauty product review is positively associated with confirmation.

3.2. Confirmation, Value, Satisfaction and Loyalty

Consumers evaluate a product based on the extent to which their expectations about the product, before experiencing it, align with the actual product. When expectations match the product or when the actual experience with the product exceeds the prior expectations, it leads to positive confirmation. If the pre-experience expectations are not met by the product, it results in negative confirmation. Achieving expectation confirmation or positive confirmation is directly associated with inducing consumer satisfaction [21]. Furthermore, purchasing products online involves taking more risks compared to offline purchases, and the expectations about the product can have a more significant impact. This is particularly true for beauty products, which, being in direct contact with the skin, often require more consideration and time investment than other types of products.
First, Bhattacherjee [21] extended Oliver’s [20] expectation confirmation model by including perceived usefulness to build the information system (IS) continuance model, which indicates the intention to use an information system continuously. In this study, we employed a similar mechanism but described the concept of perceived value of the product. Value is a concept associated with what customers gain from using a product or service. The perceived value of the product reflects how customers perceive the quality or goodness of the product. This concept can be considered a smaller or similar concept in the same context as perceived usefulness when using a system. Therefore, as in previous research, we predict that consumers will judge the value of a product based on the difference between their prior expectations and the outcomes.
Second, applying the IS continuance model to Oliver’s [20] expectation confirmation model, Bhattacherjee [21] demonstrated that perceived usefulness, along with expectation confirmation and satisfaction, positively affected the continued use of information systems. The extent to which consumers’ prior expectations match the actual product is essential for evaluating product satisfaction. For beauty products purchased offline, consumers can acquire information about the product at the time of purchase. However, when purchasing products online, customers cannot assess the product until they see it in person, which can result in significantly higher expectations. The degree of alignment between these expectations and the actual product will have a more substantial impact on product satisfaction or dissatisfaction online compared to offline. Therefore, we hypothesize the following:
H2: 
Confirmation is positively associated with the perceived value of a product.
H3: 
Confirmation is positively associated with one’s satisfaction with a product.
The idea of the perceived value of the product has been substantiated through numerous studies to influence consumer purchase intentions [41,42]. Research has also shown that it extends beyond purchase intentions, impacting various consumer behavioral intentions [27,43]. When using beauty products, users perceive value in terms of functional experiences such as moisturizing, whitening, and soothing, as well as sensory experiences including fragrance and texture, and economical value due to cost-effectiveness. Users who cannot immediately grasp the product’s value with a single use will evaluate the product over several repetitions. All these dimensions of value interact in a complex manner to form the overall evaluation of the product, which ultimately translates into satisfaction. Therefore, we hypothesize the following:
H4: 
The perceived value of a product is positively associated with one’s satisfaction with the product.
Repurchasing can be explained as a result of positively evaluating various aspects of the product from a company’s perspective, and it can be measured as performance [44,45]. In this study, we aimed to explain repurchase intention using the concepts of perceived value and satisfaction.
First, prior research has shown that perceived value not only has a positive influence on satisfaction but also affects various consumer behaviors, such as the intention to revisit and recommend [25,27,46,47]. From a consumer’s perspective, perceived value refers to the overall evaluation created based on the difference between what consumers paid and what they received [25]. Similarly to other services and products, beauty products are expected to receive a positive evaluation from consumers by providing then with a better experience than what they have paid for.
Second, Oliver [32] suggests that as the experience of satisfaction continues, loyalty increases, which aligns with marketing empirical research results indicating an increase in repurchase intentions [48,49]. Additionally, empirical evidence shows a significant association between the intention of the continued use of information systems and satisfaction [21,50]. If consumers use and are satisfied with beauty products, they are more likely to repurchase, because they want to continue experiencing the functions, effects, and other benefits of the product. Therefore, we hypothesize the following:
H5: 
The perceived value of a product is positively associated with the consumer’s repurchase intention.
H6: 
The satisfaction with a product is positively associated with the consumer’s repurchase intention.

3.3. From Online to Offline and Back

Many companies recognize the significance of the online market and make efforts to positively impact their offline performance through this channel. Therefore, it is important to examine the relationship between offline and online contexts. Companies with processes that involve booking appointments and consuming products or services online, followed by receiving products or services offline, need to accurately understand the connection between these two channels to provide customers with consistent service [51,52]. If what was expected in the purchase is felt while using the product, it is highly likely to be positively linked to the trust in the influencer who facilitated the purchase [53]. In other words, when the expectations regarding a purchased product align with the actual experience, this results in an increased level of trust in the influencer who facilitated the purchase. Consumers make purchasing decisions based on a variety of information. In this context, influencers play a role by providing diverse information, which in turn builds positive trust in their role. As consumers directly experience the discrepancy between their initial expectations and the product’s performance while using it, they can exhibit a positive attitude towards the entity that minimizes risks in the invisible online space. Therefore, it is plausible to explain that a high level of confirmation can have a positive impact on trust in the influencer.
H7: 
Confirmation is positively associated with trust in the influencer.
Next, previous research e.g., [51,54], suggests that the attitudes of users felt offline influence user attitudes online, as evidenced by the close connection between offline and online contexts. Online experiences are shaped by offline experiences, influencing satisfaction with products and services online. Experiences with products, felt directly through offline interactions, also affect the attitudes of online information creators. Online purchasing is a challenging task, as it involves making judgments about products without physically experiencing them, which can be viewed as a risk-taking behavior. In such cases, satisfaction with products purchased online leads to a stronger trust in the factors that facilitated the purchase. In this study, the factors facilitating purchases can be explained by influencers and are expected to have a positive impact on trust in the influencer in the online context. Therefore, we hypothesize the following:
H8: 
Satisfaction with a product is positively associated with trust in an influencer.

3.4. The Ripple Effect

The concept of intention to purchase other recommended products can be associated with ‘trust transference’, which suggests that when an individual forms initial trust in a particular entity, they are likely to extend their trust to other related entities [55]. This phenomenon is also reflected in social influence theory, where individuals who feel a sense of closeness to a person tend to evaluate that person similarly in new situations, without starting the evaluation process from scratch.
When users purchase a product based on information provided by influencers and find that the purchased product indeed possesses the value as described in the influencer’s review, they are more likely to maintain a consistently positive perception of the influencer’s recommendations regarding other products. Trust established online can be implicit, emphasizing the similarity and familiarity between users and influencers, which can positively influence the influencer’s subsequent actions. This concept aligns with “the riffle effect”, as it implies that the influence of a purchase does not stop at a single dimension, but extends to influence future purchases of other related products. Therefore, we hypothesize the following:
H9: 
Trust in an influencer is positively associated with the intention to purchase other recommended products by the influencer.
Based on the hypotheses mentioned above, a research model was derived as shown in Figure 1.

4. Method

4.1. Data Collection

In order to fulfill the purpose of the study, questionnaires were distributed for about two weeks to panels of survey agencies in South Korea. The subjects were individuals who had encountered beauty product review contents through social media (SNS) and had actually purchased the products. Before responding to the survey, they were asked to recall the product review by a prominent influencer that they had referred to in order to purchase ‘a certain product’. A total of 318 individuals responded, showing a response rate of approximately 32%. The characteristics of the survey subjects are shown in Table 1.

4.2. Measurement Items

To validate the research model proposed in this study, measurement instruments for seven variables, namely the beauty product review quality, confirmation, the perceived value of the product, satisfaction with the product, repurchase intention, trust in the influencer, and the intention to purchase other recommended products, were derived from previous research and appropriately adjusted to fit the context of this study. The quality of the beauty product reviews was assessed using Wang and Strong’s [17] measurement items, which were categorized into intrinsic quality with four dimensions (reliability, accuracy, objectivity, reputation), contextual quality with four dimensions (value, relevance, completeness, timeliness), expressive quality with four dimensions (interpretability, ease of understanding, expression consistency, concise expression), and accessibility quality with two dimensions (accessibility, security). The participants responded to these items with the phrase “The review of the beauty product is ~” during the survey.
The perceived value of the products was evaluated using the measurement items from Sweeney and Soutar [27], encompassing three aspects: functional value, emotional value, and economic value. The survey items were adjusted accordingly, starting with “This beauty product is ~”. Expectation–confirmation, the product satisfaction, and the purchase intentions were assessed based on the model proposed by Bhattacherjee [21]. Four items were employed for each construct. Trust in the influencer was adapted from the concepts from Dietz and Den Hartog [56], including items such as “This influencer can be trusted”, “I trust this influencer”, and “Through experience, I have trust in this influencer”. The intention to purchase other recommended products was developed based on previous research on trust transference [57]. The survey items included phrases like “I am interested in other products recommended by this influencer”, “I will positively consider purchasing other products recommended by this influencer”, “I am willing to buy anything recommended by this influencer”, and “If the opportunity arises, I would like to purchase other products recommended by this influencer”.
In addition, the survey included questions about the respondents’ demographic information such as gender, age, marital status, education, occupation, and monthly income, as well as the predominant types of SNS used and the types of beauty products they had purchased based on reviews. Except for demographic variables, all the items were measured on a 5-point Likert scale (1 = Not at all; 2 = Not so; 3 = Neutral; 4 = Somewhat; 5 = Very).

5. Results

5.1. Reliability and Validity

We used the values of Cronbach’s α and factor loading to assess the convergent validity. It is considered reliable and acceptable if a factor loading’s minimum value is greater than 0.6 [58,59]. In this process, one item for confirmation was excluded. This study explained a total of seven constructs; however, two of them (beauty product review quality and perceived value of the product) were formative variables, which made it difficult to assess their correlation or discriminant validity with the other five reflective variables [60,61]. Therefore, these two variables appeared to be excluded. Table 2 shows that all the factor loadings and α values exceeded 0.6; thus, all the items were considered reliable and valid. Also, this study achieved sufficient internal consistency because the composite reliability and the average variance extracted values (AVEs) were higher than 0.8 and 0.6, respectively. Table 3 further shows that the square root of the AVEs was greater than all the correlation coefficients among the other variables, demonstrating discriminant validity [58].

5.2. Multicollinearity

Following Hair et al.’s [62] approach, a multicollinearity test was conducted to validate a measurement model with second-order factors. There were four formative first-order factors for beauty product review quality and three formative first-order factors for perceived value, comprising 50 and 12 measurement items, respectively. Among these, the highest VIF value was 2.952 (AQA1), which was below the threshold value of 5. This indicates that there was no significant multicollinearity issue among the formative first-order factors, and therefore, it can be concluded that there were no problems in the path analysis.

5.3. Common Method Bias

Harman’s single factor was conducted for common method bias testing. The results revealed that the simultaneous loading of all the items produced a total variance of 41.8% (<50%), an acceptable maximum threshold of the total variance [63]. A more rigorous method was applied in the studies by Lindell and Whitney [64]; three marker variable items unrelated to this study were included in the questionnaire for the common method bias test. Since the correlation between the marker variable and the variables we used did not exceed 0.07 on average, we can conclude that CMB was not a concern with our data.

5.4. Structural Model Analysis

After confirming the reliability and validity of this study, the structural model analysis was conducted as the next step. The PLS method was used to conduct structural model analysis [65], which is widely used to verify hypotheses and is appropriate when the model contains a formatively structured construct.
First, the four first-order factors constituting beauty product review quality were tested to determine their validity. The standardized path coefficients for the four indicators were intrinsic quality (β = 0.340, p < 0.001), contextual quality (β = 0.380, p < 0.001), representational quality (β = 0.279, p < 0.001), and accessibility quality (β = 0.104, p < 0.01). The results showed that all the standardized path coefficients were valid, indicating the four indicators for the beauty product review quality were deemed adequate. Also, the three first-order factors constituting the perceived value of the product were tested to determine their validity. The standardized path coefficients for the three indicators were functional value (β = 0.441, p < 0.001), emotional value (β = 0.437, p < 0.001), and economical value (β = 0.293, p < 0.001). These results showed that all the standardized path coefficients were valid, indicating the four indicators for the beauty product review quality and the three indicators for the perceived value of the product were all deemed adequate.
Next, a structural model analysis was conducted using smartPLS 4, and the results are as shown in Figure 2. All nine paths were statistically significant, and all the R square values exceeded 0.440, indicating that the research model was appropriate.

5.5. Mediation Analysis

Model 6 of the PROCESS macro [66] was adopted to test the mediating effect of perceived value and satisfaction with the product. As shown in Table 4, all the pathways were significant. First, the pathway of “confirmation → perceived value → repurchase intention” was significant (indirect effect = 0.395, 95%CI = [0.169, 0.470]). Second, the pathway of “confirmation → satisfaction with the product → repurchase intention” was significant (indirect effect = 0.079, 95%CI = [0.031, 0.131]). Third, the sequential pathway of “confirmation → perceived value → satisfaction with the product → repurchase intention” was significant (indirect effect = 0.086, 95%CI = [0.030, 0.151]). Thus, more confirmation was serially associated with the higher perceived value (β =0.631, p < 0.001), satisfaction with the product (β = 0.495, p < 0.001), and, finally, repurchase intention (β = 0.276, p < 0.001).

6. Discussion

6.1. Summary of Findings

SNS plays a significant role in providing information across various fields, with a particular emphasis on the beauty industry. In the context of the beauty industry, Korean beauty products have gained global popularity, often associated with Korean pop culture content like dramas and idols. Information about these beauty products is predominantly shared through SNS platforms, and consumers rely on the information they obtain from SNS to guide their purchasing decisions. The influence of SNS-acquired information on beauty product purchases is supported by research findings, underlining the substantial role that SNS-published product reviews hold in this industry.
Online platforms excel in disseminating information quickly and widely, which places a greater emphasis on content creation. Existing studies have generally focused on the mechanisms leading to product purchases or on singular actions or roles, such as the image of influencers; however, this study is significant in that it specifically examines the effects of influencer reviews at the core of the network. By not limiting the effects of reviews to the purchase of the same product but also examining their impact on the purchase of different products, this study expands the scope of previous research. In addition to this multifaceted perspective, efforts should be made to implement strategies that account for the diverse forms of information available online. Online information includes, not only text, images, and videos, but also an understanding of which aspects users are interested in. Companies should consider ways to effectively convey the features they prioritize. This is because information quality serves as a gateway that transforms users into consumers and encourages them to consistently purchase the company’s products.
The first research question of this study, “How does online review quality contribute to inducing repurchases of products?”, explored the role of online beauty product reviews in influencing the perceived value and satisfaction of products, ultimately leading to repurchases. This mechanism has been demonstrated as part of the O2O model, showcasing how online information can shape the evaluation of products offline, culminating in the repurchase process.
The second research question was “How does the transition from online to offline and back occur, and what are its ripple effects?” This question aimed to explore the influence of online product information and the subsequent experience of performance differences on consumers in both online and offline environments. Ultimately, it has been confirmed that the information users verify offline not only influences ongoing product purchases but also positively impacts the relationships with the influencers who provided the information.

6.2. Theoretical Implications

This study offers three key theoretical insights. First, this research introduced a new perspective on information quality, particularly in the context of SNS product reviews. Previous studies often overlooked information quality in SNS product reviews, and even when it was addressed, it was typically evaluated based on DeLone and McLean’s [67] theory, which may not have reflected the consumers’ perspective adequately. This study utilized Wang and Strong’s [18] information quality theory, measuring the information quality in SNS product reviews from the consumers’ viewpoint. As a result, it was confirmed that information quality significantly influences consumers’ repurchase intentions. This perspective emphasized the importance of the relationship between the influencer providing information and the product. Second, this research expanded the conventional quality–value–satisfaction–loyalty model, a mechanism widely used in previous studies. This mechanism is prevalent in various research but has often lacked specific context and has incorporated a variety of concepts in a complex manner. This study empirically demonstrated an expanded mechanism, quality–confirmation–value–satisfaction–loyalty, by incorporating confirmation, broadening the scope of previous research, and enabling more comprehensive explanations of individual behavior in various contexts. Third, while many studies have traditionally segregated online and offline activities when investigating customer behavior, this research bridged the gap by using online platform information to explore how online review quality influences offline actions, such as influencer or customer product purchases. This approach adds clarity to the mutual relationship between online and offline contexts, providing valuable insights into the interplay between the two.
These insights contribute to the advancement of the field by providing a more comprehensive understanding of information quality, expanding the applicability of existing models, and clarifying the connection between online and offline customer behaviors.

6.3. Practical Implications

From a practical standpoint, three key takeaways emerge. First, companies should recognize the significance of delivering high-quality information to consumers. Through the marketing process, businesses should strive to offer information of a high standard, instilling trust in consumers. This trust can ultimately lead to repurchases and effective revenue growth. Collaborating with influencers can be an effective marketing strategy to fulfill this role. For instance, businesses can work with influencers to promote specific products, ensuring that they convey relevant features and information. The significance lies in the empirical demonstration of the importance of these concepts in relation to confirmation. Second, it is crucial for companies to realize that the online and offline realms are mutually linked. Companies should not limit their information about products or services to a single channel, but instead devise strategies with consistent concepts and goals across both domains. For example, when a company launches a product and wishes to emphasize new features or benefits, they should ensure that the information provided by the influencers in online reviews aligns with the customer’s experience of the product in offline interactions. This consistency is valuable, as it allows customers to share their experiences with the product, reaching a wider audience of potential customers. Third, the finding that information quality has a positive impact on product purchases can be interpreted as indicating that customers have potential expectations regarding the information they receive. Customers may become accustomed to considering a variety of information provided by companies as basic information. Therefore, if customers feel that the information provided by a company is lacking, it would be advisable for the company to structure its approach to respond in real-time through online channels, thereby enhancing the quality of information from different perspectives.
These practical insights underscore the importance of information quality and the synergy between online and offline contexts in influencing consumer behavior and enhancing business outcomes.

6.4. Limitations and Directions for Future Research

This study has several limitations that offer directions for future research. First, one limitation pertains to the authenticity and sincerity of the information shared online. Future research could focus on measuring the authenticity of information generated online, considering various indicators that could be applied in subsequent models for empirical validation. Ensuring the credibility and reliability of online information is vital in understanding its impact on consumer behavior. Second, while this study categorized beauty products into a broad group, a more detailed exploration of different categories within the beauty industry is necessary. Beauty products encompass a wide range, including skincare, makeup, haircare, nail care, and more. Consumers’ informational needs may vary by product category, so future research should consider investigating specific product categories to gain a deeper understanding of consumer behavior. Third, influencers are categorized into four main groups based on their reach, namely mega (over one million followers), macro (tens of thousands to hundreds of thousands), micro (thousands to tens of thousands), and nano (less than a thousand). It is crucial to acknowledge that the influence and target audience of influencers may vary across these categories. Therefore, future research should consider the impact of influencers based on their magnitude within specific product categories to provide more nuanced insights into the consumer decision-making process.
These limitations highlight potential avenues for further research, enabling a more comprehensive understanding of consumer behavior in the context of online beauty product reviews and influencer marketing.

7. Conclusions

According to existing research, customers’ attitudes can vary depending on the composition of the reviews that include attractive information for purchasing a product. This study adds the concept of influencers, who have a significant network effect online, to examine the mechanisms from both the perspective of product purchases and the role of influencers. This confirms that the role of influencers in online marketing continues to be emphasized.
With the increasing share of online purchases, online information has become a crucial factor for consumers. This study found that online review quality positively influences perceived value, satisfaction, and repurchase intention when the expectations for a product matches reality. Furthermore, the confirmation of online reviews facilitates the transition from online to offline and back, which in turn generates ripple effects. The results of this study not only add clarity to the interrelationship between online and offline contexts but also provide implications for businesses in formulating strategies to leverage influencers for product sales.

Author Contributions

Conceptualization, S.-S.K.; literature reviews, H.-J.S. and A.K.; methodology, S.-S.K.; software, S.-S.K.; validation, S.-S.K.; formal analysis, S.-S.K.; investigation, H.-J.S. and S.-S.K.; resources, H.-J.S.; data curation, H.-J.S.; writing—original draft preparation, H.-J.S. and A.K.; writing—review and editing, S.-S.K.; visualization, A.K. and S.-S.K.; supervision, S.-S.K.; project administration, S.-S.K. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Ethical review and approval were waived for this study, as it did not involve any interventions or procedures that required ethical approval.

Informed Consent Statement

This study involved a survey targeting human participants. The survey was anonymous, and participants were informed about consent prior to beginning the survey.

Data Availability Statement

The data presented in this study are available upon request to the corresponding author.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Hassenzahl, M.; Tractinsky, N. User Experience-A Research Agenda. Behav. Inf. Technol. 2006, 25, 91–97. [Google Scholar] [CrossRef]
  2. Katona, Z.; Zubcsek, P.P.; Sarvary, M. Network Effects and Personal Influences: The Diffusion of an Online Social Network. J. Mark. Res. 2011, 48, 425–443. [Google Scholar] [CrossRef]
  3. Zuo, S. Strategies on the Live Broadcast Marketing Strategy of Anhua Dark Tea Based on Sicas Model. Front. Bus. Econ. Manag. 2023, 10, 219–222. [Google Scholar] [CrossRef]
  4. Zhou, S.; Guo, B. The Order Effect on Online Review Helpfulness: A Social Influence Perspective. Decis. Support Syst. 2017, 93, 77–87. [Google Scholar] [CrossRef]
  5. Macheka, T.; Quaye, E.S.; Ligaraba, N. The Effect of Online Customer Reviews and Celebrity Endorsement on Young Female Consumers’ Purchase Intentions. Young Consum. 2024, 25, 462–482. [Google Scholar] [CrossRef]
  6. Arief, M.; Mustikowati, R.I.; Chrismardani, Y. Why Customers Buy an Online Product? The Effects of Advertising Attractiveness, Influencer Marketing and Online Customer Reviews. LBS J. Manag. Res. 2023, 21, 81–99. [Google Scholar] [CrossRef]
  7. Glucksman, M. The Rise of Social Media Influencer Marketing on Lifestyle Branding: A Case Study of Lucie Fink. Elon J. Undergrad. Res. Commun. 2017, 8, 77–87. [Google Scholar]
  8. Jin, S.V.; Muqaddam, A.; Ryu, E. Instafamous and Social Media Influencer Marketing. Mark. Intell. Plan. 2019, 37, 567–579. [Google Scholar] [CrossRef]
  9. Hermanda, A.; Sumarwan, U.; Tinaprillia, N. The Effect of Social Media Influencer on Brand Image, Self-Concept, and Purchase Intention. J. Consum. Sci. 2019, 4, 76–89. [Google Scholar] [CrossRef]
  10. Masuda, H.; Han, S.H.; Lee, J. Impacts of Influencer Attributes on Purchase Intentions in Social Media Influencer Marketing: Mediating Roles of Characterizations. Technol. Forecast. Soc. Change 2022, 174, 121246. [Google Scholar] [CrossRef]
  11. Gallarza, M.G.; Gil Saura, I.; Arteaga Moreno, F. The Quality—Value Satisfaction—Loyalty Chain: Relationships and Impacts. Tour. Rev. 2013, 68, 3–20. [Google Scholar] [CrossRef]
  12. Kim, H.; Jeon, G.; Chung, J.Y. Understanding the Role of Follower Size in Influencer Marketing: Examining the Perspective of Source Credibility and Attribution Theory. J. Curr. Issues Res. Advert. 2024, 45, 320–338. [Google Scholar] [CrossRef]
  13. Kim, Y.J.; Hollingshead, A.B. Online Social Influence: Past, Present, and Future. Ann. Int. Commun. Assoc. 2015, 39, 163–192. [Google Scholar] [CrossRef]
  14. Cooley, D.; Parks-Yancy, R. The Effect of Social Media on Perceived Information Credibility and Decision Making. J. Internet Commer. 2019, 18, 249–269. [Google Scholar] [CrossRef]
  15. Huffaker, D.A. Dimensions of Leadership and Social Influence in Online Communities. Hum. Commun. Res. 2010, 36, 593–617. [Google Scholar] [CrossRef]
  16. Hughes, C.; Swaminathan, V.; Brooks, G. Driving Brand Engagement Through Online Social Influencers: An Empirical Investigation of Sponsored Blogging Campaigns. J. Mark. 2019, 83, 78–96. [Google Scholar] [CrossRef]
  17. Wang, R.W.; Strong, D.M. Beyond accuracy: What Data Quality Means to Data Consumers. J. Manag. Inf. Syst. 1996, 12, 5–33. [Google Scholar] [CrossRef]
  18. Lurie, N.H. Decision Making in Information-Rich Environments: The Role of Information Structure. J. Consum. Res. 2004, 30, 473–486. [Google Scholar] [CrossRef]
  19. Bapna, R.; Umyarov, A. Do Your Online Friends Make You Pay? A Randomized Field Experiment on Peer Influence in Online Social Networks. Manag. Sci. 2015, 61, 1902–1920. [Google Scholar] [CrossRef]
  20. Oliver, R.L. A Cognitive Model of the Antecedents and Consequences of Satisfaction Decisions. J. Mark. Res. 1980, 17, 460–469. [Google Scholar] [CrossRef]
  21. Bhattacherjee, A. Understanding Information Systems Continuance: An Expectation Confirmation Model. MIS Q. 2001, 23, 351–370. [Google Scholar] [CrossRef]
  22. Davis, F.D. Perceived Usefulness, Perceived Ease of Use, And User Acceptance of Information Technology. MIS Q. 1989, 13, 319–340. [Google Scholar] [CrossRef]
  23. Buzzell, R.; Gale, B. The PIMS Principles; Free Press: New York, NY, USA, 1987. [Google Scholar]
  24. McDougall, G.H.; Levesque, T. Customer Satisfaction with Service: Putting Perceived Value into the Equation. J. Serv. Mark. 2000, 14, 392–410. [Google Scholar] [CrossRef]
  25. Zeithaml, V.A. Consumer Perception of Price, Quality, and Value: A Means-End Model and Synthesis of Evidence. J. Mark. 1988, 52, 2–22. [Google Scholar] [CrossRef]
  26. Kim, S.S. Purchase Intention in the Online Open Market: Do Concerns for E-Commerce Really Matter? Sustainability 2020, 12, 773. [Google Scholar] [CrossRef]
  27. Sweeney, J.C.; Soutar, G.N. Consumer Perceived Value: The Development of a Multiple Item Scale. J. Retail. 2001, 77, 203–220. [Google Scholar] [CrossRef]
  28. Wang, Y.; Po Lo, H.; Chi, R.; Yang, Y. An Integrated Framework for Customer Value and Customer--Relationship--Management Performance: A Customer--Based Perspective from China. Manag. Serv. Qual. Int. J. 2004, 14, 169–182. [Google Scholar] [CrossRef]
  29. Sánchez-Fernández, R.; Iniesta-Bonillo, M.Á. The Concept of Perceived Value: A Systematic Review of The Research. Mark. Theory 2007, 7, 427–451. [Google Scholar] [CrossRef]
  30. Granados, J.C.; Pérez, L.M.; Pedraza-Rodríguez, J.A.; Gallarza, M.G. Revisiting the Quality-Value-Satisfaction-Loyalty Chain for Corporate Customers in the Travel Agency Sector. Eur. J. Tour. Res. 2021, 27, 2711. [Google Scholar] [CrossRef]
  31. Oliver, R.L. Varieties of Value in the Consumption Satisfaction Response. ACR North Am. Adv. 1996, 23, 143. [Google Scholar]
  32. Oliver, R.L. Value as Excellence in the Consumption Experience. In Consumer Value. A Framework for Analysis and Research; Holbrook, M.B., Ed.; Routledge: London, UK, 1999; pp. 43–62. [Google Scholar]
  33. Kim, Y.; Wang, Q.; Roh, T. Do Information and Service Quality Affect Perceived Privacy Protection, Satisfaction, and Loyalty? Evidence from a Chinese O2O-based Mobile Shopping Application. Telemat. Inform. 2021, 56, 101483. [Google Scholar] [CrossRef]
  34. Wang, Y.S.; Lin, S.J.; Li, C.R.; Tseng, T.H.; Li, H.T.; Lee, J.Y. Developing and Validating a Physical Product E-Tailing Systems Success Model. Inf. Technol. Manag. 2018, 19, 245–257. [Google Scholar] [CrossRef]
  35. Mckinney, V.; Kanghyun, K.; Zahedi, F.M. The Measurement of Web-Customer Satisfaction: An Expectation and Disconfirmation Approach. Inf. Syst. Res. 2002, 13, 296–315. [Google Scholar] [CrossRef]
  36. Shewfelt, R.L. What is Quality? Postharvest Biol. Technol. 1999, 15, 197–200. [Google Scholar] [CrossRef]
  37. Cheng, Y.M. Extending the Expectation-confirmation Model with Quality and Flow to Explore Nurses’ Continued Blended E-learning Intention. Inf. Technol. People 2014, 27, 230–258. [Google Scholar] [CrossRef]
  38. Hu, Y.; Koren, Y.; Volinsky, C. Collaborative Filtering for Implicit Feedback Datasets. In Proceedings of the 2008 Eighth IEEE International Conference on Data Mining, Pisa, Italy, 15–19 December 2008; pp. 263–272. [Google Scholar]
  39. Barabasi, A.L.; Albert, R. Emergence of Scaling in Random Networks. Science 1999, 286, 509–512. [Google Scholar] [CrossRef]
  40. Wright, P.M. Making Great Theories. J. Manag. Stud. 2017, 54, 384–390. [Google Scholar] [CrossRef]
  41. Gan, C.; Wang, W. The Influence of Perceived Value on Purchase Intention in Social Commerce Context. Internet Res. 2017, 27, 772–785. [Google Scholar] [CrossRef]
  42. Shafiq, R.; Raza, I.; Zia-ur-Rehman, M. Analysis of The Factors Affecting Customers’ Purchase Intention: The Mediating Role of Perceived Value. Afr. J. Bus. Manag. 2011, 5, 10577. [Google Scholar]
  43. Wu, L.Y.; Chen, K.Y.; Chen, P.Y.; Cheng, S.L. Perceived Value, Transaction Cost, and Repurchase-Intention in Online Shopping: A Relational Exchange Perspective. J. Bus. Res. 2014, 67, 2768–2776. [Google Scholar] [CrossRef]
  44. Mittal, V.; Ross Jr, W.T.; Baldasare, P.M. The Asymmetric Impact of Negative and Positive Attribute-Level Performance on Overall Satisfaction and Repurchase Intentions. J. Mark. 1998, 62, 33–47. [Google Scholar] [CrossRef]
  45. Yi, Y.; La, S. What Influences the Relationship between Customer Satisfaction and Repurchase Intention? Investigating the Effects of Adjusted Expectations and Customer Loyalty. Psychol. Mark. 2004, 21, 351–373. [Google Scholar] [CrossRef]
  46. Bolton, R.N.; Drew, J.H. A Multistage Model of Customers’ Assessments of Service Quality and Value. J. Consum. Res. 1991, 17, 375–384. [Google Scholar] [CrossRef]
  47. Sheth, J.N.; Newman, B.I.; Gross, B.L. Why We Buy What We Buy: A Theory of Consumption Values. J. Bus. Res. 1991, 22, 159–170. [Google Scholar] [CrossRef]
  48. Cronin Jr, J.J.; Brady, M.K.; Hult, G.T.M. Assessing the Effects of Quality, Value, and Customer Satisfaction on Consumer Behavioral Intentions in Service Environments. J. Retail. 2000, 76, 193–218. [Google Scholar] [CrossRef]
  49. Oliver, R.L. Cognitive, Affective, And Attribute Bases of The Satisfaction Response. J. Consum. Res. 1993, 20, 418–430. [Google Scholar] [CrossRef]
  50. Lin, C.S.; Wu, S.; Tsai, R.J. Integrating Perceived Playfulness into Expectation-Confirmation Model for Web Portal Context. Inf. Manag. 2005, 42, 683–693. [Google Scholar] [CrossRef]
  51. Choi, H.Y.; Kim, S.S. How is Satisfaction with Online-to-offline App Formed? Importance of Confirmation through Offline Experience. SAGE Open 2022, 12, 21582440221134899. [Google Scholar] [CrossRef]
  52. Kim, S.S. The Effect of Trust on the Continuance Use of Kakao Hairshop: A Perspective of Satisfaction Transfer through Confirmation. J. Internet Electron. Commer. Res. 2023, 23, 153–169. [Google Scholar] [CrossRef]
  53. Arkanuddin, M.F.; Abi Firmansyah, M.; Fakhruddin, M.B.; Dewani, C.H.; Kridaningsih, T.E. The Analysis of Satisfaction on Digital Business Sector: Expectation Confirmation Model Validation. Ekombis Rev. J. Ilm. Ekon. Dan Bisnis 2023, 11, 1781–1800. [Google Scholar] [CrossRef]
  54. Kim, S.S.; Choi, J.Y.; Koo, C. Effects of ICTs in Mega Events on National Image Formation: The Case of PyeongChang Winter Olympic Games in South Korea. J. Hosp. Tour. Technol. 2022, 13, 217–239. [Google Scholar] [CrossRef]
  55. Kim, S.S.; Kim, M. The Impact of Online Open Market Platform Trust on Seller Trust, Transaction Satisfaction, and Intention to Use: Focus on Trust Transference. Korea Serv. Manag. Soc. 2019, 20, 211–233. [Google Scholar]
  56. Dietz, G.; Den Hartog, D.N. Measuring Trust Inside Organisations. Pers. Rev. 2006, 35, 557–588. [Google Scholar] [CrossRef]
  57. Rotter, J.B. Interpersonal Trust, Trustworthiness, and Gullibility. Am. Psychol. 1980, 35, 1–7. [Google Scholar] [CrossRef]
  58. Fornell, C.; Larcker, D.F. Evaluating Structural Equation Models with Unobservable Variables and Measurement Error. J. Mark. Res. 1981, 18, 39–50. [Google Scholar] [CrossRef]
  59. Nunnally, J.C. Psychometric Theory, (2nd ed); McGraw Hill: New York, NY, USA, 1978. [Google Scholar]
  60. Freeze, R.; Raschke, R.L. An Assessment of Formative and Reflective Constructs in IS Research. In Proceedings of the Fifteenth European Conference on Information Systems, St. Gallen, Switzerland, 7–9 June 2007. [Google Scholar]
  61. Hanafiah, M.H. Formative vs. Reflective Measurement Model: Guidelines for Structural Equation Modeling Research. Int. J. Anal. Appl. 2020, 18, 876–889. [Google Scholar]
  62. Hair, J.F.; Hult, G.T.M.; Ringle, C.M.; Sarstedt, M. A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM); Sage Publications: New York, NY, USA, 2014. [Google Scholar]
  63. Podsakoff, P.M.; MacKenzie, S.B.; Lee, J.-Y.; Podsakoff, N.P. Common Method Biases in Behavioral Research: A Critical Review of the Literature and Recommended Remedies. J. Appl. Psychol. 2003, 88, 879–903. [Google Scholar] [CrossRef]
  64. Lindell, M.K.; Whitney, D.J. Accounting for Common Method Variance in Cross-Sectional Research Designs. J. Appl. Psychol. 2001, 86, 114–121. [Google Scholar] [CrossRef]
  65. Sarstedt, M.; Ringle, C.M.; Hair, J.F. Partial Least Squares Structural Equation Modeling. In Handbook of Market Research; Springer International Publishing: Cham, Germany, 2021; pp. 587–632. [Google Scholar]
  66. Hayes, A.F. Introduction to Mediation, Moderation, and Conditional Process Analysis: A Regression-Based Approach; Guilford Press: New York, NY, USA, 2013. [Google Scholar]
  67. DeLone, W.H.; McLean, E.R. The DeLone and McLean Model of Information Systems Success: A Ten-Year Update. J. Manag. Inf. Syst. 2003, 19, 9–30. [Google Scholar]
Jtaer 19 00155 g001
Figure 1. Research model.
Figure 1. Research model.
Jtaer 19 00155 g001
Jtaer 19 00155 g002
Figure 2. Results of structural model test.
Figure 2. Results of structural model test.
Jtaer 19 00155 g002
Table 1. Characteristics of the respondents (N = 318).
Table 1. Characteristics of the respondents (N = 318).
CriteriaFreq.%CriteriaFreq.%
GenderMale8627.0EducationMiddle school154.7
Female23273.0High school6319.8
Agebelow 203310.4Bachelor’s degree21467.3
20s8426.4Master’s degree268.2
30s6520.4Monthly income ($)Below 10 K5818.2
40s9128.610~20 K4514.2
50s4514.220~30 K7724.2
OccupationStudent5015.730~40 K5717.9
Office job10432.740~50 K299.1
Public official123.850~60 K154.7
Self-ownership309.460~70 K206.3
Professionals3611.3Above 70 K175.3
Sales/Service226.9Marital statusMarried15448.4
Housewife4112.9Not married16150.6
Etc.237.2Etc.30.9
SNS
type		Instagram10533.0Beauty product typeMakeup10131.8
YouTube12940.6Hair5316.7
Blog5316.7Skin care15649.1
Facebook247.5Nail care82.5
Etc.72.2
Table 2. Reliability and validity analysis.
Table 2. Reliability and validity analysis.
ConstructsItemsLoadingsCACRAVE
Confirmation
(CF)		CF10.8550.8100.8760.642
CF20.641
CF30.813
CF40.876
Intention to purchase other recommended products
(OPI)		OPI10.8260.8500.8990.691
OPI20.849
OPI30.778
OPI40.868
Repurchase intention
(RPI)		RPI10.8920.9020.9320.773
RPI20.883
RPI30.896
RPI40.846
Satisfaction with the product
(SAT)		SAT10.8730.8390.9030.756
SAT20.888
SAT30.848
Trust in influence
(TR)		TR10.8790.8470.9080.766
TR20.872
TR30.875
Note: CA = Cronbach’s α; CR = composite reliability; AVE = average variance extracted.
Table 3. Correlation and discriminant validity.
Table 3. Correlation and discriminant validity.
CFOPIRPISATTR
CF0.801
OPI0.5500.831
RPI0.7740.5730.879
SAT0.7310.5560.6180.870
TR0.6390.6970.6020.6300.875
Note: bold numbers in diagonal are root of AVE.
Table 4. The total, direct, and indirect effects.
Table 4. The total, direct, and indirect effects.
EffectβSELLCIULCI
Total effect0.901 ***0.0480.8070.995
Direct effects
   CF→PV0.631 ***0.0320.5670.695
   PV→SAT0.495 ***0.0510.3960.595
   CF→SAT0.286 ***0.0430.2010.371
   PV→RPI0.626 ***0.0790.4690.782
   SAT→RPI0.276 ***0.0770.1230.428
   CF→RPI0.341 ***0.0630.2160.466
Indirect effects0.560 ***0.0710.4250.700
   CF→PV→RPI0.3950.0660.2690.529
   CF→SAT→RPI0.0790.0260.0310.131
   CF→PV→SAT→RPI0.0860.0310.0300.151
Note: SE = standard errors; LLCI = lower-level confidence interval; ULCI = upper-level confidence interval. CF = confirmation; PV = perceived value; SAT = satisfaction with the product; RPI = repurchase intention. *** p < 0.001.
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MDPI and ACS Style

Sim, H.-J.; Kim, A.; Kim, S.-S. Interplay Between Online and Offline Realms: Examining Influencers’ Impact and Ripple Effects on Beauty Product Sales. J. Theor. Appl. Electron. Commer. Res. 2024, 19, 3197-3213. https://doi.org/10.3390/jtaer19040155

AMA Style

Sim H-J, Kim A, Kim S-S. Interplay Between Online and Offline Realms: Examining Influencers’ Impact and Ripple Effects on Beauty Product Sales. Journal of Theoretical and Applied Electronic Commerce Research. 2024; 19(4):3197-3213. https://doi.org/10.3390/jtaer19040155

Chicago/Turabian Style

Sim, Hee-Jin, Ahyun Kim, and Sang-Soo Kim. 2024. "Interplay Between Online and Offline Realms: Examining Influencers’ Impact and Ripple Effects on Beauty Product Sales" Journal of Theoretical and Applied Electronic Commerce Research 19, no. 4: 3197-3213. https://doi.org/10.3390/jtaer19040155

APA Style

Sim, H. -J., Kim, A., & Kim, S. -S. (2024). Interplay Between Online and Offline Realms: Examining Influencers’ Impact and Ripple Effects on Beauty Product Sales. Journal of Theoretical and Applied Electronic Commerce Research, 19(4), 3197-3213. https://doi.org/10.3390/jtaer19040155

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